1.
Connectivism:
Education & Artificial Intelligence
Alaa AlDahdouh

2.
Research Question
• Does the advancement in Artificial Neural Network (ANN)
studies support connectivism’s assumptions?

3.
What is Artificial Neural Network (ANN)
• Artificial Neural Network (ANN) is a software structure
developed based on concepts inspired by biological functions
of brain; it aims at creating machines able to learn like human
(Goodfellow, Bengio, & Courville, 2016; Nielsen, 2015; Russell
& Norvig, 2010).

4.
What is Artificial Neuron?
• Artificial Neuron is a node that receives input from preceding
neurons and makes a decision to 'fire' to the next neurons.

5.
What is Artificial Neuron?
• Suppose that you are a neuron and you want to make a
decision to buy a car. In your perspective, car insurance and
gas price are more important than parking cost. Therefore, a
car would cost you $575 per month. But you set a threshold to
buy a car if it would cost you less than $480. Therefore, you
make a decision not to buy a car.
• Your own perspectives of
inputs and the threshold
are called weights and bias
respectively.

6.
Artificial Neuron & Connectivism
• Connectivists often argue that a person is like a neuron and
they both live in networks but in different levels (Downes,
2016).
• As each neuron has its own bias, each person has his/her
own internal judgement system. Siemens (2005, 2006) states
that knowledge and learning rests in diversity of options and
Aldahdouh et al. (2015) argues that educational systems
should foster the learners' diversity, not their similarity.

7.
Artificial Neuron & Connectivism
• In order to be able to learn, ANN should move slowly and smoothly from
one decision to another. For these reasons, ANN researchers have
examined many alternative soft activation functions such as sigmoid .
• Connectivism has been
criticized for its
oversimplification of
interaction between nodes as
the connection can be either
active or inactive (Clarà &
Barberà, 2014). However,
connectivism proponents
(Aldahdouh et al., 2015) have
shown that a connection is
graded and not necessarily
sharp.

8.
What is ANN Architecture?
• ANN Architecture refers to the way of arranging neurons in certain
order to make it easier for a learning algorithm to find the biases and
weights.
ANN levels of abstraction
ANN as a person’s brain
ANN as a group of learners
Architecture levels of abstraction
Learner's inner abilities and
mental capacities
Designing learning-
environment

9.
What is ANN Architecture?
• The most common ANN architectures can be divided based on
three criteria:
– Number of layers
– Flow of information
– Neuron connectivity

10.
ANN Architecture: Number of Layers
Shallow Neural Network Deep Neural Network

11.
ANN Architecture: Number of Layers
• The terms shallow and deep are somehow misleading because
they are not in line with educational terminology of surface and
deep learning (Vermunt & Vermetten, 2004).
• The concept of layers is completely incompatible with
connectivism’s assumptions. The idea of that a network consists
of a sequence of layers contradicts with chaos theory which is
one of the underpinning theories of connectivism (Aldahdouh,
2017).

12.
ANN Architecture: Flow of Information
Feedforward neural network Recurrent neural network

13.
ANN Architecture: Flow of Information
• Flow of information and connection directionality are some of
subjects discussed in connectivism literature. Aldahdouh et al.
(2015) showed that some connections in knowledge network are
bidirectional while others unidirectional. They also showed that
"The node can connect to itself" (p. 5).
• Information in connectivism flows in both directions which
means feedforward architecture contradicts with connectivism’s
principles while recurrent architecture agrees with it.

14.
ANN Architecture: Node Connectivity
Fully connected neural network Convolutional neural network

15.
ANN Architecture: Node Connectivity
• Connectivism appreciates network connectivity and seeks to
increase it as much as possible. Actually, connectivism defines
learning as the process of connecting nodes in a network
(Aldahdouh et al., 2015; Siemens, 2005). This may indicate that
connectivism aims to make a learner as a node in the fully
connected network.
• However, it has been proved that increasing connectivity adds
complexity to ANN. A convolutional network, on the other hand,
decreases the connectivity and achieves better results.
Connectivists should pay attention to this because it disagrees
with their main network designs (Downes, 2010a).
• One can argue that connectivism agrees with fully connected
network but disagrees with convolutional network.

16.
Learning Algorithm: Training Set
• Suppose you have a very simple neuron with one input and one
output. To teach this neuron to memorize a multiplication table
for number 5, ANN researchers usually give it a so-called
training set.
• A Training Set contains a
number of different input values
(1, 2, 3, 4, 5, 6 ...) paired with the
correct output (5, 10, 15, 20, 25,
30 ...).

17.
Learning Algorithm: Training Set
• The labeled training set which contains the input values along
with correct output assumes knowledge as something static and
something we know in advance. Learning algorithm is not
allowed to manipulate inputs or correct outputs in any case
(Nielsen, 2015).
• This limits the ability of ANN to learn something previously
known, not to discover something new.
• The idea of static knowledge contradicts with connectivism’s
principle of dynamic knowledge (Aldahdouh et al., 2015).

18.
Learning Algorithm: One Neuron Training
• The neuron receives
input and generates
output according to its
own weight (w) and
bias (b) which were
randomly selected.
This means, the output
of the neuron (as)
would most probably
differ from the correct
output (ys).
Cost
If the Cost goes to
zero, then we found
the right w and b.

19.
Learning Algorithm: One Neuron Training
• Assume a neuron = a person => The inputs of the neuron would
represent the current learning experiences. The correct outputs
represent the reality (ontology) and neuron outputs represent a
person's perceptions about the reality (epistemology).
• The Cost represents the gap between learner's perceptions and
the reality. That is to say, learning is the process of minimizing
the gap between learner's perceptions and the reality.
• Of course, this definition perfectly fits constructivist theory of
learning (Piaget, 2013).

20.
Learning Algorithm: Finding the minimum Cost

21.
Learning Algorithm: Finding the minimum Cost
• A learning rate (η) refers to the speed of learning outcome. Or how
fast a learner should learn. The learning rate should not be too fast
that makes a learner jump from point to point; long jumps disrupt
learning. A learning rate should not be very slow too; it makes a learner
crawl in detail that would not serve him to achieve his goal. Finding
the right pace of learning is a difficult task that depends on the initial
state of the learner’s perspectives and bias.
• Learning rate is one of many other free parameters which are left free
for human and outside of ANN’s control.
• Adjusting free parameters is the responsibility of consciousness.
• Connectivism is also criticized for its ambiguity in that it does not
show how pattern recognition is done (Aldahdouh et al., 2015).

22.
Learning Algorithm: Network Level
• In a network of millions of neurons, what are those right weights
and bias that a single neuron learns? What is the meaning of
those connections and biases? Why does each neuron connect to
other neurons in that way? Until now no one has a theory.
• Learning in network level clearly supports the core assumption of
connectivism: A single connection between two nodes does not
have meaning in its own. A meaning is distributed across group of
connections or patterns (Aldahdouh et al., 2015; Downes, 2006).
Looking at the network from higher level mitigates its complexity
(Downes, 2016). But that does not give us the answer and the
exact meaning of the entities in the lower level.

23.
Aldahdouh, A. A. (2017). Does Artificial Neural Network
support Connectivism’s assumptions? International
Journal of Instructional Technology and Distance Learning,
14(3), 3–26.
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Alaa AlDahdouh